AI0-001 AI Concepts and Foundations Practice Question
A data scientist trains a linear regression model to predict house prices. The model has high bias and low variance. Which action would most likely reduce bias?
⚠ Common exam trap
CompTIA often tests the bias-variance tradeoff by making candidates confuse regularization (which reduces variance) with methods that reduce bias, or by implying that more data always fixes underfitting.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add polynomial features
High bias indicates the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding polynomial features increases model complexity by introducing non-linear terms, which allows the linear regression model to better fit the training data and thus reduce bias.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply L2 regularization
Why it's wrong here
Regularization increases bias to reduce variance.
- ✗
Increase the training dataset size
Why it's wrong here
More data typically reduces variance, not bias.
- ✓
Add polynomial features
Why this is correct
Adding complexity reduces bias but may increase variance.
- ✗
Remove irrelevant features
Why it's wrong here
Removing features simplifies the model, increasing bias.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 754 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.